A fast and high-quality charge model for the next generation general AMBER force field

Supramolecular citrate poly allylamine hydrochloride nanoparticles for citrate delivery and calcium oxalate nanocrystal dissolution

HYPOTHESIS

Renal calculi (kidney stones) are mainly made by calcium oxalate and can cause different complications including malfunction of the kidney. The most important urinary stone inhibitors are citrate molecules. Unfortunately, the amount of citrate reaching the kidney after oral ingestion is low. We hypothesized that nanoparticles of polyallylamine hydrochloride (CIT-PAH) carrying citrate ions could simultaneously deliver citrates while PAH would complex oxalate triggering dissolution and removal of CaOx nanocrystals.

EXPERIMENTS

We successfully prepared nanoparticles of citrate ions with polyallylamine hydrochloride (CIT-PAH), PAH with oxalate (OX-PAH) and characterize them by Small Angle X ray Scattering (SAXS), Transmission Electron Microscopy (TEM), Dynamic Light Scattering (DLS) and NMR. Dissolution of CaOx nanocrystals in presence of CIT-PAH have been followed with Wide Angle Xray Scattering (WAXS), DLS and Confocal Raman Microscopy. Raman spectroscopy was used to study the dissolution of crystals in synthetic urine samples. The release of citrate from CIT-PAH was followed by diffusion NMR. Molecular dynamics (MD) simulations were carried out to study the interaction of CIT and OX ions with PAH.

FINDINGS

CIT-PAH nanoparticles dissolves CaOx nanocrystals as shown by NMR, DLS, TEM and WAXS in water and by Raman spectroscopy in artificial human urine. WAXS and Raman show that the crystal structure of CaOx disappears in the presence of CIT-PAH. DLS shows that the time required for CaOX dissolution will depend on the concentration of CIT-PAH NPs. NMR proves that citrate ions are released from the CIT PAH NPs during CaOX dissolution, MD simulations showed that oxalates exhibit a stronger interaction for PAH than citrate, explaining the removal of oxalate ions and replacement of the citrate in the polymer nanoparticles.

Gut microbiota biotransformation of drug glucuronides leading to gastrointestinal toxicity: Therapeutic potential of bacterial β-glucuronidase inhibition in mycophenolate-induced enteropathy

AIMS

Drug-induced enteropathy is often associated with the therapeutic use of certain glucuronidated drugs. One such drug is mycophenolic acid (MPA), a well-established immunosuppressant of which gastrointestinal adverse effects are a major concern. The role of bacterial β-glucuronidase (β-G) from the gut microbiota in MPA-induced enteropathy has recently been discovered. Bacterial β-G hydrolyzes MPAG, the glucuronide metabolite of MPA excreted in the bile, leading to the digestive accumulation of MPA that would favor in turn these adverse events. We therefore hypothesized that taming bacterial β-G activity might reduce MPA digestive exposure and prevent its toxicity.

MAIN METHODS

By using a multiscale approach, we evaluated the effect of increasing concentrations of MPA on intestinal epithelial cells (Caco-2 cell line) viability, proliferation, and migration. Then, we investigated the inhibitory properties of amoxapine, a previously described bacterial β-G inhibitor, by using molecular dynamics simulations, and evaluated its efficiency in blocking MPAG hydrolysis in an Escherichia coli-based β-G activity assay. The pharmacological effect of amoxapine was evaluated in a mouse model.

KEY FINDINGS

We observed that MPA impairs intestinal epithelial cell homeostasis. Amoxapine efficiently blocks the hydrolysis of MPAG to MPA and significantly reduces digestive exposure to MPA in mice. As a result, administration of amoxapine in MPA-treated mice significantly attenuated gastrointestinal lesions.

SIGNIFICANCE

Collectively, these results suggest that the digestive accumulation of MPA is involved in the pathophysiology of MPA-gastrointestinal adverse effects. This study provides a proof-of-concept of the therapeutic potential of bacterial β-G inhibitors in glucuronidated drug-induced enteropathy.

Coupling molecular density functional theory with converged selected configuration interaction methods to study excited states in aqueous solution

This paper presents the first implementation of a coupling between advanced wavefunction theories and molecular density functional theory (MDFT). This method enables the modeling of solvent effect into quantum mechanical (QM) calculations by incorporating an electrostatic potential generated by solvent charges into the electronic Hamiltonian. Solvent charges are deduced from the spatially and angularly dependent solvent particle density. Such a density is obtained through the minimization of the functional associated with the molecular mechanics (MM) Hamiltonian describing the interaction between the fluid particles. The introduced QM/MDFT framework belongs to QM/MM family of methods, but its originality lies in the use of MDFT as the MM solver, offering two main advantages. First, its functional formulation makes it competitive with respect to sampling-based molecular mechanics. Second, it preserves a molecular-level description lost in macroscopic continuum approaches. The excited state properties of water and formaldehyde molecules solvated into water have been computed at the selected configuration interaction (SCI) level. The excitation energies and dipole moments have been compared with experimental data and previous theoretical work. A key finding is that using the Hartree-Fock method to describe the solute allows for predicting the solvent charge around the ground state with sufficient precision for the subsequent SCI calculations of excited states. This significantly reduces the computational cost of the described procedure, paving the way for the study of more complex molecules.

Software Infrastructure for Next-Generation QM/MM-ΔMLP Force Fields

We present software infrastructure for the design and testing of new quantum mechanical/molecular mechanical and machine-learning potential (QM/MM-ΔMLP) force fields for a wide range of applications. The software integrates Amber's molecular dynamics simulation capabilities with fast, approximate quantum models in the xtb package and machine-learning potential corrections in DeePMD-kit. The xtb package implements the recently developed density-functional tight-binding QM models with multipolar electrostatics and density-dependent dispersion (GFN2-xTB), and the interface with Amber enables their use in periodic boundary QM/MM simulations with linear-scaling QM/MM particle-mesh Ewald electrostatics. The accuracy of the semiempirical models is enhanced by including machine-learning correction potentials (ΔMLPs) enabled through an interface with the DeePMD-kit software. The goal of this paper is to present and validate the implementation of this software infrastructure in molecular dynamics and free energy simulations. The utility of the new infrastructure is demonstrated in proof-of-concept example applications. The software elements presented here are open source and freely available. Their interface provides a powerful enabling technology for the design of new QM/MM-ΔMLP models for studying a wide range of problems, including biomolecular reactivity and protein-ligand binding.

Molecular Modeling of Single- and Double-Hydrocarbon-Stapled Coiled-Coil Inhibitors against Bcr-Abl: Toward a Treatment Strategy for CML

The chimeric oncoprotein Bcr-Abl is the causative agent of virtually all chronic myeloid leukemias and a subset of acute lymphoblastic leukemias. As a result of the so-called Philadelphia chromosome translocation t(9;22), Bcr-Abl manifests as a constitutively active tyrosine kinase, which promotes leukemogenesis by activation of cell cycle signaling pathways. Constitutive and oncogenic activation is mediated by an N-terminal coiled-coil oligomerization domain in Bcr (Bcr-CC), presenting a therapeutic target for inhibition of Bcr-Abl activity toward the treatment of Bcr-Abl+ leukemias. Previously, we demonstrated that a rationally designed Bcr-CC mutant, CCmut3, exerts a dominant negative effect upon Bcr-Abl activity by preferential oligomerization with Bcr-CC. Moreover, we have shown that conjugation to a leukemia-specific cell-penetrating peptide (CPP-CCmut3) improves intracellular delivery and activity. However, our full-length CPP-CCmut3 construct (81 aa) is encumbered by an intrinsically high degree of conformational variability and susceptibility to proteolytic degradation relative to traditional small-molecule therapeutics. Here, we iterate a new generation of CCmut3 inhibitors against Bcr-CC-mediated Bcr-Abl assembly designed to address these constraints through incorporation of all-hydrocarbon staples spanning i and i + 7 positions in α-helix 2 (CPP-CCmut3-st). We utilize computational modeling and biomolecular simulation to evaluate single- and double-stapled CCmut3 candidates in silico for dynamics and binding energetics. We further model a truncated system characterized by the deletion of α-helix 1 and the flexible loop linker, which are known to impart high conformational variability. To study the impact of the N-terminal cyclic CPP toward model stability and inhibitor activity, we also model the full-length and truncated systems devoid of the CPP, with a cyclized CPP, and with an open-configuration CPP, for a total of six systems that comprise our library. From this library, we present lead-stapled peptide candidates to be synthesized and evaluated experimentally as our next iteration of inhibitors against Bcr-Abl.

Identification of pseudobaptigenin as a novel polyphenol-based multi-target antagonist of different hormone receptors for breast cancer therapeutics

Breast cancer (BC) is one of the most prevalent cancers in the world and is one of the major reasons for the death of women worldwide. BC is majorly categorized based on the presence or absence of three cell receptors ER, PR and HER2. The latest treatment for BC involves interfering with the production and action of hormones such as estrogen and progesterone. These hormones bind with receptors such as ER and PR and enhance the growth and proliferation of the BC cells. Although the available are effective, the increasing resistance and side effects related to hormonal imbalance are significant and hence there is a need for designing. On the other hand, plant-derivative products have gained a lot of popularity for their promising anti-cancerous activities. Polyphenols are one such group of plant derivatives that have proven to be useful against cancer. In the present study, an in-silico approach was used to search for a polyphenol that can inhibit ER. In this work, a total of 750 polyphenols were taken into consideration. This number was narrowed down to 55, based on their ADMET properties. These 55 polyphenols were then docked to the receptors, ER, PR and HER2. The molecular docking was followed by Molecular Dynamics (MD) simulations. Based on molecular docking and MD simulation results it was concluded that Pseudobaptigenin has the potential to be an inhibitor of ER, PR and HER2.Communicated by Ramaswamy H. Sarma.

Structure-Activity Studies of 1,2,4-Oxadiazoles for the Inhibition of the NAD+-Dependent Lysine Deacylase Sirtuin 2

The NAD+-dependent lysine deacylase sirtuin 2 (Sirt2) is involved in multiple pathological conditions such as cancer. Targeting Sirt2 has thus received an increased interest for therapeutic purposes. Furthermore, the orthologue from Schistosoma mansoni (SmSirt2) has been considered for the potential treatment of the neglected tropical disease schistosomiasis. We previously identified a 1,2,4-oxadiazole-based scaffold from the screening of the "Kinetobox" library as a dual inhibitor of human Sirt2 (hSirt2) and SmSirt2. Herein, we describe the structure-activity studies on 1,2,4-oxadiazole-based analogues, which are potent inhibitors of human Sirt2 deacetylation. As proposed by docking studies, a substrate-competitive and cofactor-noncompetitive binding mode of inhibition could be determined in vitro via binding assays and kinetic analysis and further confirmed by a crystal structure of an oxadiazole inhibitor in complex with hSirt2. Optimized analogues reduced cell viability and inhibited prostate cancer cell migration, in correlation with Sirt2 deacetylase inhibition both in vitro and in cells.

Adsorption of Flavonoids in a Transcriptional Regulator TtgR: Relative Binding Free Energies and Intermolecular Interactions

Antimicrobial resistance in bacteria often arises from their ability to actively identify and expel toxic compounds. The bacterium strain Pseudomonas putida DOT-T1E utilizes its TtgABC efflux pump to confer robust resistance against antibiotics, flavonoids, and organic solvents. This resistance mechanism is intricately regulated at the transcriptional level by the TtgR protein. Through molecular dynamics and alchemical free energy simulations, we systematically examine the binding of seven flavonoids and their derivatives with the TtgR transcriptional regulator. Our simulations reveal distinct binding geometries and free energies for the flavonoids in the active site of the protein, which are driven by a range of noncovalent forces encompassing van der Waals, electrostatic, and hydrogen bonding interactions. The interplay of molecular structures, substituent patterns, and intermolecular interactions effectively stabilizes the bound flavonoids, confining their movements within the TtgR binding pocket. These findings yield valuable insights into the molecular determinants that govern ligand recognition in TtgR and shed light on the mechanism of antimicrobial resistance in P. putida DOT-T1E.

CHARMM-GUI QM/MM Interfacer for a Quantum Mechanical and Molecular Mechanical (QM/MM) Simulation Setup: 1. Semiempirical Methods

Quantum mechanical (QM) treatments, when combined with molecular mechanical (MM) force fields, can effectively handle enzyme-catalyzed reactions without significantly increasing the computational cost. In this context, we present CHARMM-GUI QM/MM Interfacer, a web-based cyberinfrastructure designed to streamline the preparation of various QM/MM simulation inputs with ligand modification. The development of QM/MM Interfacer has been achieved through integration with existing CHARMM-GUI modules, such as PDB Reader and Manipulator, Solution Builder, and Membrane Builder. In addition, new functionalities have been developed to facilitate the one-stop preparation of QM/MM systems and enable interactive and intuitive ligand modifications and QM atom selections. QM/MM Interfacer offers support for a range of semiempirical QM methods, including AM1(+/d), PM3(+/PDDG), MNDO(+/d, +/PDDG), PM6, RM1, and SCC-DFTB, tailored for both AMBER and CHARMM. A nontrivial setup related to ligand modification, link-atom insertion, and charge distribution is automatized through intuitive user interfaces. To illustrate the robustness of QM/MM Interfacer, we conducted QM/MM simulations of three enzyme-substrate systems: dihydrofolate reductase, insulin receptor kinase, and oligosaccharyltransferase. In addition, we have created three tutorial videos about building these systems, which can be found at https://www.charmm-gui.org/demo/qmi. QM/MM Interfacer is expected to be a valuable and accessible web-based tool that simplifies and accelerates the setup process for hybrid QM/MM simulations.

Structural basis of tolvaptan binding to the vasopressin V2 receptor

The vasopressin V2 receptor (V2R) is a validated therapeutic target for autosomal dominant polycystic kidney disease (ADPKD), with tolvaptan being the first FDA-approved antagonist. Herein, we used Gaussian accelerated molecular dynamics simulations to investigate the spontaneous binding of tolvaptan to both active and inactive V2R conformations at the atomic-level. Overall, the binding process consists of two stages. Tolvaptan binds initially to extracellular loops 2 and 3 (ECL2/3) before overcoming an energy barrier to enter the pocket. Our simulations result highlighted key residues (e.g., R181, Y205, F287, F178) involved in this process, which were experimentally confirmed by site-directed mutagenesis. This work provides structural insights into tolvaptan-V2R interactions, potentially aiding the design of novel antagonists for V2R and other G protein-coupled receptors.

Inclusion of Water Multipoles into the Implicit Solvation Framework Leads to Accuracy Gains

The current practical "workhorses" of the atomistic implicit solvation─the Poisson-Boltzmann (PB) and generalized Born (GB) models─face fundamental accuracy limitations. Here, we propose a computationally efficient implicit solvation framework, the Implicit Water Multipole GB (IWM-GB) model, that systematically incorporates the effects of multipole moments of water molecules in the first hydration shell of a solute, beyond the dipole water polarization already present at the PB/GB level. The framework explicitly accounts for coupling between polar and nonpolar contributions to the total solvation energy, which is missing from many implicit solvation models. An implementation of the framework, utilizing the GAFF force field and AM1-BCC atomic partial charges model, is parametrized and tested against the experimental hydration free energies of small molecules from the FreeSolv database. The resulting accuracy on the test set (RMSE ∼ 0.9 kcal/mol) is 12% better than that of the explicit solvation (TIP3P) treatment, which is orders of magnitude slower. We also find that the coupling between polar and nonpolar parts of the solvation free energy is essential to ensuring that several features of the IWM-GB model are physically meaningful, including the sign of the nonpolar contributions.

Current State of Open Source Force Fields in Protein-Ligand Binding Affinity Predictions

In drug discovery, the in silico prediction of binding affinity is one of the major means to prioritize compounds for synthesis. Alchemical relative binding free energy (RBFE) calculations based on molecular dynamics (MD) simulations are nowadays a popular approach for the accurate affinity ranking of compounds. MD simulations rely on empirical force field parameters, which strongly influence the accuracy of the predicted affinities. Here, we evaluate the ability of six different small-molecule force fields to predict experimental protein-ligand binding affinities in RBFE calculations on a set of 598 ligands and 22 protein targets. The public force fields OpenFF Parsley and Sage, GAFF, and CGenFF show comparable accuracy, while OPLS3e is significantly more accurate. However, a consensus approach using Sage, GAFF, and CGenFF leads to accuracy comparable to OPLS3e. While Parsley and Sage are performing comparably based on aggregated statistics across the whole dataset, there are differences in terms of outliers. Analysis of the force field reveals that improved parameters lead to significant improvement in the accuracy of affinity predictions on subsets of the dataset involving those parameters. Lower accuracy can not only be attributed to the force field parameters but is also dependent on input preparation and sampling convergence of the calculations. Especially large perturbations and nonconverged simulations lead to less accurate predictions. The input structures, Gromacs force field files, as well as the analysis Python notebooks are available on GitHub.

Statistical Coupling Analysis Predicts Correlated Motions in Dihydrofolate Reductase

The role of dynamics in enzymatic function is a highly debated topic. Dihydrofolate reductase (DHFR), due to its universality and the depth with which it has been studied, is a model system in this debate. Myriad previous works have identified networks of residues in positions near to and remote from the active site that are involved in dynamics and others that are important for catalysis. For example, specific mutations on the Met20 loop in E. coli DHFR (N23PP/S148A) are known to disrupt millisecond-timescale motions and reduce catalytic activity. However, how and if networks of dynamically coupled residues influence the evolution of DHFR is still an unanswered question. In this study, we first identify, by statistical coupling analysis and molecular dynamic simulations, a network of coevolving residues, which possess increased correlated motions. We then go on to show that allosteric communication in this network is selectively knocked down in N23PP/S148A mutant E. coli DHFR. Finally, we identify two sites in the human DHFR sector which may accommodate the Met20 loop double proline mutation while preserving dynamics. These findings strongly implicate protein dynamics as a driving force for evolution.

Identification and Validation of Compounds Targeting Leishmania major Leucyl-Aminopeptidase M17

Leishmaniasis is a neglected tropical disease; there is currently no vaccine and treatment is reliant upon a handful of drugs suffering from multiple issues including toxicity and resistance. There is a critical need for development of new fit-for-purpose therapeutics, with reduced toxicity and targeting new mechanisms to overcome resistance. One enzyme meriting investigation as a potential drug target in Leishmania is M17 leucyl-aminopeptidase (LAP). Here, we aimed to chemically validate LAP as a drug target in L. major through identification of potent and selective inhibitors. Using RapidFire mass spectrometry, the compounds DDD00057570 and DDD00097924 were identified as selective inhibitors of recombinant Leishmania major LAP activity. Both compounds inhibited in vitro growth of L. major and L. donovani intracellular amastigotes, and overexpression of LmLAP in L. major led to reduced susceptibility to DDD00057570 and DDD00097924, suggesting that these compounds specifically target LmLAP. Thermal proteome profiling revealed that these inhibitors thermally stabilized two M17 LAPs, indicating that these compounds selectively bind to enzymes of this class. Additionally, the selectivity of the inhibitors to act on LmLAP and not against the human ortholog was demonstrated, despite the high sequence similarities LAPs of this family share. Collectively, these data confirm LmLAP as a promising therapeutic target for Leishmania spp. that can be selectively inhibited by drug-like small molecules.

Targeting host O-linked glycan biosynthesis affects Ebola virus replication efficiency and reveals differential GalNAc-T acceptor site preferences on the Ebola virus glycoprotein

Ebola virus glycoprotein (EBOV GP) is one of the most heavily O-glycosylated viral glycoproteins, yet we still lack a fundamental understanding of the structure of its large O-glycosylated mucin-like domain and to what degree the host O-glycosylation capacity influences EBOV replication. Using tandem mass spectrometry, we identified 47 O-glycosites on EBOV GP and found similar glycosylation signatures on virus-like particle- and cell lysate-derived GP. Furthermore, we performed quantitative differential O-glycoproteomics on proteins produced in wild-type HEK293 cells and cell lines ablated for the three key initiators of O-linked glycosylation, GalNAc-T1, -T2, and -T3. The data show that 12 out of the 47 O-glycosylated sites were regulated, predominantly by GalNAc-T1. Using the glycoengineered cell lines for authentic EBOV propagation, we demonstrate the importance of O-linked glycan initiation and elongation for the production of viral particles and the titers of progeny virus. The mapped O-glycan positions and structures allowed to generate molecular dynamics simulations probing the largely unknown spatial arrangements of the mucin-like domain. The data highlight targeting GALNT1 or C1GALT1C1 as a possible way to modulate O-glycan density on EBOV GP for novel vaccine designs and tailored intervention approaches.IMPORTANCEEbola virus glycoprotein acquires its extensive glycan shield in the host cell, where it is decorated with N-linked glycans and mucin-type O-linked glycans. The latter is initiated by a family of polypeptide GalNAc-transferases that have different preferences for optimal peptide substrates resulting in a spectrum of both very selective and redundant substrates for each isoform. In this work, we map the exact locations of O-glycans on Ebola virus glycoprotein and identify subsets of sites preferentially initiated by one of the three key isoforms of GalNAc-Ts, demonstrating that each enzyme contributes to the glycan shield integrity. We further show that altering host O-glycosylation capacity has detrimental effects on Ebola virus replication, with both isoform-specific initiation and elongation playing a role. The combined structural and functional data highlight glycoengineered cell lines as useful tools for investigating molecular mechanisms imposed by specific glycans and for steering the immune responses in future vaccine designs.

Resolving Photoinduced Femtosecond Three-Dimensional Solute-Solvent Dynamics through Surface Hopping Simulations

Photoinduced dynamics in solution is governed by mutual solute-solvent interactions, which give rise to phenomena like solvatochromism, the Stokes shift, dual fluorescence, or charge transfer. Understanding these phenomena requires simulating the solute's photoinduced dynamics and simultaneously resolving the three-dimensional solvent distribution dynamics. If using trajectory surface hopping (TSH) to this aim, thousands of trajectories are required to adequately sample the time-dependent three-dimensional solvent distribution functions, and thus resolve the solvent dynamics with sub-Ångstrom and femtosecond accuracy and sufficiently low noise levels. Unfortunately, simulating thousands of trajectories with TSH in the framework of hybrid quantum mechanical/molecular mechanical (QM/MM) can be prohibitively expensive when employing ab initio electronic structure methods. To tackle this challenge, we recently introduced a computationally efficient approach that combines efficient linear vibronic coupling models with molecular mechanics (LVC/MM) via electrostatic embedding [Polonius et al., JCTC 2023, 19, 7171-7186]. This method provides solvent-embedded, nonadiabatically coupled potential energy surfaces while scaling similarly to MM force fields. Here, we employ TSH with LVC/MM to unravel the photoinduced dynamics of two small thiocarbonyl compounds solvated in water. We describe how to estimate the number of trajectories required to produce nearly noise-free three-dimensional solvent distribution functions and present an analysis based on approximately 10,000 trajectories propagated for 3 ps. In the electronic ground state, both molecules exhibit in-plane hydrogen bonds to the sulfur atom. Shortly after excitation, these bonds are broken and reform perpendicular to the molecular plane on timescales that differ by an order of magnitude due to steric effects. We also show that the solvent relaxation dynamics is coupled to the electronic dynamics, including intersystem crossing. These findings are relevant to advance the understanding of the coupled solute-solvent dynamics of solvated photoexcited molecules, e.g., biologically relevant thio-nucleobases.

Exploring the selectivity of cytochrome P450 for enhanced novel anticancer agent synthesis

Cytochrome P450 monooxygenases are an extensive and unique class of enzymes, which can regio- and stereo-selectively functionalise hydrocarbons by way of oxidation reactions. These enzymes are naturally occurring but have also been extensively applied in a synthesis context, where they are used as efficient biocatalysts. Recently, a biosynthetic pathway where a cytochrome P450 monooxygenase catalyses a critical step of the pathway was uncovered, leading to the production of a number of products that display high antitumour potency. In this work, we use computational techniques to gain insight into the factors that determine the relative yields of the different products. We use conformational search algorithms to understand the substrate stereochemistry. On a machine-learned 3D protein structure, we use molecular docking to obtain a library of favourable poses for substrate-protein interaction. With molecular dynamics, we investigate the most favourable poses for reactivity on a molecular level, allowing us to investigate which protein-substrate interactions favour a given product and thus gain insight into the product selectivity.

Identification of Small Molecule Ligand Binding Sites On and In the ARNT PAS-B Domain

Transcription factors are generally challenging to target with small molecule inhibitors due to their structural plasticity and lack of catalytic sites. Notable exceptions include several naturally ligand-regulated transcription factors, including our prior work with the heterodimeric HIF-2 transcription factor which showed that small molecule binding within an internal pocket of the HIF-2α PAS-B domain can disrupt its interactions with its dimerization partner, ARNT. Here, we explore the feasibility of similarly targeting small molecules to the analogous ARNT PAS-B domain itself, potentially opening a promising route to simultaneously modulate several ARNT-mediated signaling pathways. Using solution NMR screening of an in-house fragment library, we previously identified several compounds that bind ARNT PAS-B and, in certain cases, antagonize ARNT association with the TACC3 transcriptional coactivator. However, these ligands have only modest binding affinities, complicating characterization of their binding sites. We address this challenge by combining NMR, MD simulations, and ensemble docking to identify ligand-binding 'hotspots' on and within the ARNT PAS-B domain. Our data indicate that the two ARNT/TACC3 inhibitors, KG-548 and KG-655, bind to a β-sheet surface implicated in both HIF-2 dimerization and coactivator recruitment. Furthermore, while KG-548 binds exclusively to the β-sheet surface, KG-655 can additionally bind within a water-accessible internal cavity in ARNT PAS-B. Finally, KG-279, while not a coactivator inhibitor, exemplifies ligands that preferentially bind only to the internal cavity. All three ligands promoted ARNT PAS-B homodimerization, albeit to varying degrees. Taken together, our findings provide a comprehensive overview of ARNT PAS-B ligand-binding sites and may guide the development of more potent coactivator inhibitors for cellular and functional studies.

Molecular Insight into the Processes and Mechanisms of N2 Adsorption and Accumulation at the Hydrophobic Solid/Liquid Interface

In this study, molecular dynamics (MD) simulations were employed to elucidate the processes and underlying mechanisms that govern the adsorption and accumulation of gas (represented by N2) at the hydrophobic solid-liquid interface, using the GROMACS program with an AMBER force field. Our findings indicate that, regardless of surface roughness, the presence of water molecules is a prerequisite for the adsorption and aggregation of N2 molecules on solid surfaces. N2 molecules dissolved in water can cluster even without a solid substrate. In the gas-solid-liquid system, the exclusion of water molecules at the hydrophobic solid-liquid interface and the adsorption of N2 molecules do not occur simultaneously. A loosely arranged layer of water molecules is initially formed on the hydrophobic solid surface. The two-stage process of N2 molecule adsorption and accumulation at the hydrophobic solid/liquid interface involves initial adsorption to the solid surface, displacing water molecules, followed by N2 accumulation via self-interaction after saturating the substrate's surface. The process and underlying mechanisms of gas adsorption and accumulation at hydrophobic solid/liquid interfaces elucidated in this study offer a molecular-level understanding of nano-gas layer formation.

Unveiling Conformational States of CDK6 Caused by Binding of Vcyclin Protein and Inhibitor by Combining Gaussian Accelerated Molecular Dynamics and Deep Learning

CDK6 plays a key role in the regulation of the cell cycle and is considered a crucial target for cancer therapy. In this work, conformational transitions of CDK6 were identified by using Gaussian accelerated molecular dynamics (GaMD), deep learning (DL), and free energy landscapes (FELs). DL finds that the binding pocket as well as the T-loop binding to the Vcyclin protein are involved in obvious differences of conformation contacts. This result suggests that the binding pocket of inhibitors (LQQ and AP9) and the binding interface of CDK6 to the Vcyclin protein play a key role in the function of CDK6. The analyses of FELs reveal that the binding pocket and the T-loop of CDK6 have disordered states. The results from principal component analysis (PCA) indicate that the binding of the Vcyclin protein affects the fluctuation behavior of the T-loop in CDK6. Our QM/MM-GBSA calculations suggest that the binding ability of LQQ to CDK6 is stronger than AP9 with or without the binding of the Vcyclin protein. Interaction networks of inhibitors with CDK6 were analyzed and the results reveal that LQQ contributes more hydrogen binding interactions (HBIs) and hot interaction spots with CDK6. In addition, the binding pocket endures flexibility changes from opening to closing states and the Vcyclin protein plays an important role in the stabilizing conformation of the T-loop. We anticipate that this work could provide useful information for further understanding the function of CDK6 and developing new promising inhibitors targeting CDK6.

FFLUX molecular simulations driven by atomic Gaussian process regression models

Machine learning (ML) force fields are revolutionizing molecular dynamics (MD) simulations as they bypass the computational cost associated with ab initio methods but do not sacrifice accuracy in the process. In this work, the GPyTorch library is used to create Gaussian process regression (GPR) models that are interfaced with the next-generation ML force field FFLUX. These models predict atomic properties of different molecular configurations that appear in a progressing MD simulation. An improved kernel function is utilized to correctly capture the periodicity of the input descriptors. The first FFLUX molecular simulations of ammonia, methanol, and malondialdehyde with the updated kernel are performed. Geometry optimizations with the GPR models result in highly accurate final structures with a maximum root-mean-squared deviation of 0.064 Å and sub-kJ mol-1 total energy predictions. Additionally, the models are tested in 298 K MD simulations with FFLUX to benchmark for robustness. The resulting energy and force predictions throughout the simulation are in excellent agreement with ab initio data for ammonia and methanol but decrease in quality for malondialdehyde due to the increased system complexity. GPR model improvements are discussed, which will ensure the future scalability to larger systems.

Nature-inspired substituted 3-(imidazol-2-yl) morpholines targeting human topoisomerase IIα: Dynophore-derived discovery

The molecular nanomachine, human DNA topoisomerase IIα, plays a crucial role in replication, transcription, and recombination by catalyzing topological changes in the DNA, rendering it an optimal target for cancer chemotherapy. Current clinical topoisomerase II poisons often cause secondary tumors as side effects due to the accumulation of double-strand breaks in the DNA, spurring the development of catalytic inhibitors. Here, we used a dynamic pharmacophore approach to develop catalytic inhibitors targeting the ATP binding site of human DNA topoisomerase IIα. Our screening of a library of nature-inspired compounds led to the discovery of a class of 3-(imidazol-2-yl) morpholines as potent catalytic inhibitors that bind to the ATPase domain. Further experimental and computational studies identified hit compound 17, which exhibited selectivity against the human DNA topoisomerase IIα versus human protein kinases, cytotoxicity against several human cancer cells, and did not induce DNA double-strand breaks, making it distinct from clinical topoisomerase II poisons. This study integrates an innovative natural product-inspired chemistry and successful implementation of a molecular design strategy that incorporates a dynamic component of ligand-target molecular recognition, with comprehensive experimental characterization leading to hit compounds with potential impact on the development of more efficient chemotherapies.

Protein Binding Leads to Reduced Stability and Solvated Disorder in the Polystyrene Nanoparticle Corona

Understanding the conformation of proteins in the nanoparticle corona has important implications in how organisms respond to nanoparticle-based drugs. These proteins coat the nanoparticle surface, and their properties will influence the nanoparticle's interaction with cell targets and the immune system. While some coronas are thought to be disordered, two key unanswered questions are the degree of disorder and solvent accessibility. Here, a model is developed for protein corona disorder in polystyrene nanoparticles of varying size. For two different proteins, it is found that binding affinity decreases as nanoparticle size increases. The stoichiometry of binding, along with changes in the hydrodynamic size, supports a highly solvated, disordered protein corona anchored at a small number of attachment sites. The scaling of the stoichiometry versus nanoparticle size is consistent with disordered polymer dimensions. Moreover, it is found that proteins are destabilized less in the presence of larger nanoparticles, and hydrophobic exposure decreases at lower curvatures. The observations hold for proteins on flat polystyrene surfaces, which have the lowest hydrophobic exposure. The model provides an explanation for previous observations of increased amyloid fibrillation rates in the presence of larger nanoparticles, and it may rationalize how cell receptors can recognize protein disorder in therapeutic nanoparticles.

Synthesis and biological evaluation of orally active anti-Trypanosoma agents

In previous studies, we developed anti-trypanosome tubulin inhibitors with promising in vitro selectivity and activity against Human African Trypanosomiasis (HAT). However, for such agents, oral activity is crucial. This study focused on further optimizing these compounds to enhance their ligand efficiency, aiming to reduce bulkiness and hydrophobicity, which should improve solubility and, consequently, oral bioavailability. Using Trypanosoma brucei brucei cells as the parasite model and human normal kidney cells and mouse macrophage cells as the host model, we evaluated 30 new analogs synthesized through combinatorial chemistry. These analogs have fewer aromatic moieties and lower molecular weights than their predecessors. Several new analogs demonstrated IC50s in the low micromolar range, effectively inhibiting trypanosome cell growth without harming mammalian cells at the same concentration. We conducted a detailed structure-activity relationship (SAR) analysis and a docking study to assess the compounds' binding affinity to trypanosome tubulin homolog. The results revealed a correlation between binding energy and anti-Trypanosoma activity. Importantly, compound 7 displayed significant oral activity, effectively inhibiting trypanosome cell proliferation in mice.

Identification of small molecular inhibitors of SIRT3 by computational and biochemical approaches a potential target of breast cancer

Sirtuin 3 (SIRT3) belongs to the Sirtuin protein family, which consists of NAD+-dependent lysine deacylase, involved in the regulation of various cellular activities. Dysregulation of SIRT3 activity has been linked to several types of cancer, including breast cancer. Because of its ability to stimulate adaptive metabolic pathways, it can aid in the survival and proliferation of breast cancer cells. Finding new chemical compounds targeted towards SIRT3 was the primary goal of the current investigation. Virtual screening of ~ 800 compounds using molecular docking techniques yielded 8 active hits with favorable binding affinities and poses. Docking studies verified that the final eight compounds formed stable contacts with the catalytic domain of SIRT3. Those compounds have good pharmacokinetic/dynamic properties and gastrointestinal absorption. Based on excellent pharmacokinetic and pharmacodynamic properties, two compounds (MI-44 and MI-217) were subjected to MD simulation. Upon drug interaction, molecular dynamics simulations demonstrate mild alterations in the structure of proteins and stability. Binding free energy calculations revealed that compounds MI-44 (- 45.61 ± 0.064 kcal/mol) and MI-217 (- 41.65 ± 0.089 kcal/mol) showed the maximum energy, suggesting an intense preference for the SIRT3 catalytic site for attachment. The in-vitro MTT assay on breast cancer cell line (MDA-MB-231) and an apoptotic assay for these potential compounds (MI-44/MI-217) was also performed, with flow cytometry to determine the compound's ability to cause apoptosis in breast cancer cells. The percentage of apoptotic cells (including early and late apoptotic cells) increased from 1.94% in control to 79.37% for MI-44 and 85.37% for MI-217 at 15 μM. Apoptotic cell death was effectively induced by these two compounds in a flow cytometry assay indicating them as a good inhibitor of human SIRT3. Based on our findings, MI-44 and MI-217 merit additional investigation as possible breast cancer therapeutics.

Binding of Phosphate Species to Ca2+ and Mg2+ in Aqueous Solution

Phosphate derivatives and their interaction with metal cations are involved in many important biological phenomena, so an accurate characterization of the phosphate-metal interaction is necessary to properly understand the role of phosphate-metal contacts in mediating biological function. Herein, we improved the standard 12-6 Lennard-Jones (LJ) potential via the usage of the 12-6-4 LJ model, which incorporates ion-induced dipole interactions. Via parameter scanning, we fine-tuned the 12-6-4 LJ polarizability values to obtain accurate absolute binding free energies for the phosphate anions H2PO4-, HPO42-, PO43- coordinating with Ca2+ and Mg2+. First, we modified the phosphate 12-6-4 LJ parameters to reproduce the solvation free energies of the series of phosphate anions using the thermodynamic integration (TI) method. Then, using the potential mean force (PMF) method, the polarizability of the metal-phosphate interaction was obtained. We show that the free energy profiles of phosphate ions coordinated to Ca2+ and Mg2+ generally show similar trends at longer metal-phosphate distances, while the absolute binding energy values increased with deprotonation. The resulting parameters demonstrate the flexibility of the 12-6-4 LJ-type nonbonded model and its usefulness in accurately describing cation-anion interactions.

Analysis of the interaction of antimalarial agents with Plasmodium falciparum glutathione reductase through molecular mechanical calculations

CONTEXT

Malaria remains a significant global health challenge with emerging resistance to current treatments. Plasmodium falciparum glutathione reductase (PfGR) plays a critical role in the defense mechanisms of malaria parasites against oxidative stress. In this study, we investigate the potential of targeting PfGR with conventional antimalarials and dual drugs combining aminoquinoline derivatives with GR inhibitors, which reveal promising interactions between PfGR and studied drugs. The naphthoquinone Atovaquone demonstrated particularly high affinity and potential dual-mode binding with the enzyme active site and cavity. Furthermore, dual drugs exhibit enhanced binding affinity, suggesting their efficacy in inhibiting PfGR, where the aliphatic ester bond (linker) is essential for effective binding with the enzyme's active site. Overall, this research provides important insights into the interactions between antimalarial agents and PfGR and encourages further exploration of its role in the mechanisms of action of antimalarials, including dual drugs, to enhance antiparasitic efficacy.

METHODS

The drugs were tested as PfGR potential inhibitors via molecular docking on AutoDock 4, which was performed based on the preoptimized structures in HF/3-21G-PCM level of theory on ORCA 5. Drug-receptor systems with the most promising binding affinities were then studied with a molecular dynamic's simulation on AMBER 16. The molecular dynamics simulations were performed with a 100 ns NPT ensemble employing GAFF2 forcefield in the temperature of 310 K, integration time step of 2 fs, and non-bond cutoff distance of 6.0 Å.

Molecular Dynamic Simulation To Reveal the Mechanism Underlying MGL-3196 Resistance to Thyroxine Receptor Beta

Thyroxine receptor beta (TRβ) is a ligand-dependent nuclear receptor that participates in regulating multiple biological processes, particularly playing an important role in lipid metabolism regulation. TRβ is currently a popular therapeutic target for nonalcoholic steatohepatitis (NASH), while no drugs have been approved to treat this disease. MGL-3196 (Resmetirom) is the first TRβ agonist that has succeeded in phase III clinical trials for the treatment of NASH; therefore, studying its molecular mechanism of action is of great significance. In this study, we employed molecular dynamic simulation to investigate the interaction mode between MGL-3196 and TRβ at the all-atom level. More importantly, by comparing the binding patterns of MGL-3196 in several prevalent TRβ mutants, it was identified that the mutations R243Q and H435R located, respectively, around and within the ligand-binding pocket of TRβ cause TRβ to be insensitive to MGL-3196. This indicates that patients with NASH carrying these two mutations may exhibit resistance to the medication of MGL-3196, thereby highlighting the potential impact of TRβ mutations on TRβ-targeted treatment of NASH and beyond.

Alchemical Enhanced Sampling with Optimized Phase Space Overlap

An alchemical enhanced sampling (ACES) method has recently been introduced to facilitate importance sampling in free energy simulations. The method achieves enhanced sampling from Hamiltonian replica exchange within a dual topology framework while utilizing new smoothstep softcore potentials. A common sampling problem encountered in lead optimization is the functionalization of aromatic rings that exhibit distinct conformational preferences when interacting with the protein. It is difficult to converge the distribution of ring conformations due to the long time scale of ring flipping events; however, the ACES method addresses this issue by modeling the syn and anti ring conformations within a dual topology. ACES thereby samples the conformer distributions by alchemically tunneling between states, as opposed to traversing a physical pathway with a high rotational barrier. We demonstrate the use of ACES to overcome conformational sampling issues involving ring flipping in ML300-derived noncovalent inhibitors of SARS-CoV-2 Main Protease (Mpro). The demonstrations explore how the use of replica exchange and the choice of softcore selection affects the convergence of the ring conformation distributions. Furthermore, we examine how the accuracy of the calculated free energies is affected by the degree of phase space overlap (PSO) between adjacent states (i.e., between neighboring λ-windows) and the Hamiltonian replica exchange acceptance ratios. Both of these factors are sensitive to the spacing between the intermediate states. We introduce a new method for choosing a schedule of λ values. The method analyzes short "burn-in" simulations to construct a 2D map of the nonlocal PSO. The schedule is obtained by optimizing an alchemical pathway on the 2D map that equalizes the PSO between the λ intervals. The optimized phase space overlap λ-spacing method (Opt-PSO) leads to more numerous end-to-end single passes and round trips due to the correlation between PSO and Hamiltonian replica exchange acceptance ratios. The improved exchange statistics enhance the efficiency of ACES method. The method has been implemented into the FE-ToolKit software package, which is freely available.

An Eutectic Mixture in the Tetrabutylammonium Bromide-Octanol System: Macroscopic and Microscopic Points of View

An eutectic mixture of tetrabutylammonium bromide and octanol in the molar ratio 1-10 exhibited a melting point of -17 °C. This system was investigated by means of infrared spectroscopy, in the liquid and in the solid state. Classical molecular dynamics was performed to study the fine details of the hydrogen bond interactions established in the mixture. Both octanol and the mixtures displayed an almost featureless far-infrared spectrum in the liquid state but it becomes highly structured in the solid phase. DFT calculations suggest that new vibrational modes appearing in the mixture at low temperatures may be related to the population of the higher energy conformers of the alcohol. Mid-infrared spectroscopy measurements evidenced no shift of the CH stretching bands in the mixture compared to the starting materials, while the OH stretching are blue shifted by a few cm-1. Consistently, molecular dynamics provides a picture of the mixture in which part of the hydrogen bonds (HB) of pure octanol is replaced by weaker HB formed with the Br anion. Due to these interactions the ionic couple becomes more separated. In agreement with this model, the lengths of all HB are much larger than those observed in mixtures containing acids reported in previous studies.

GraphEGFR: Multi-task and transfer learning based on molecular graph attention mechanism and fingerprints improving inhibitor bioactivity prediction for EGFR family proteins on data scarcity

The proteins within the human epidermal growth factor receptor (EGFR) family, members of the tyrosine kinase receptor family, play a pivotal role in the molecular mechanisms driving the development of various tumors. Tyrosine kinase inhibitors, key compounds in targeted therapy, encounter challenges in cancer treatment due to emerging drug resistance mutations. Consequently, machine learning has undergone significant evolution to address the challenges of cancer drug discovery related to EGFR family proteins. However, the application of deep learning in this area is hindered by inherent difficulties associated with small-scale data, particularly the risk of overfitting. Moreover, the design of a model architecture that facilitates learning through multi-task and transfer learning, coupled with appropriate molecular representation, poses substantial challenges. In this study, we introduce GraphEGFR, a deep learning regression model designed to enhance molecular representation and model architecture for predicting the bioactivity of inhibitors against both wild-type and mutant EGFR family proteins. GraphEGFR integrates a graph attention mechanism for molecular graphs with deep and convolutional neural networks for molecular fingerprints. We observed that GraphEGFR models employing multi-task and transfer learning strategies generally achieve predictive performance comparable to existing competitive methods. The integration of molecular graphs and fingerprints adeptly captures relationships between atoms and enables both global and local pattern recognition. We further validated potential multi-targeted inhibitors for wild-type and mutant HER1 kinases, exploring key amino acid residues through molecular dynamics simulations to understand molecular interactions. This predictive model offers a robust strategy that could significantly contribute to overcoming the challenges of developing deep learning models for drug discovery with limited data and exploring new frontiers in multi-targeted kinase drug discovery for EGFR family proteins.

Adaptive lambda schemes for efficient relative binding free energy calculation

The relative free energy perturbation (RFEP) calculation is one of the most theoretically sound computational chemistry approaches for the binding affinity prediction. However, its application is often hindered by the complexity of the calculation choices and the requirement of expertise in the field. Improper lambda scheme of RFEP may result in deviations from an accurate description of the perturbation process and is prone to erroneous affinity predictions. To address such challenges, an automated adaptive lambda method is proposed where the adaptive lambda schemes are obtained through a split-and-merge algorithm based on the pilot runs. The newly established workflow along with a series of improvements to the perturbation settings increases the consistency of the RFEP calculation results. Comparing the pilot and adaptive lambda schemes, the latter demonstrated improvements in convergence and reproducibility and lowered the mean unsigned error and the root-mean-square error. Overall, the adaptive lambda method is a reliable and robust choice to predict small molecule relative binding free energy and can be capitalized to benefit routine RFEP calculations for drug discovery projects.

Structural investigation of Trypanosoma cruzi Akt-like kinase as drug target against Chagas disease

According to the World Health Organization, Chagas disease (CD) is the most prevalent poverty-promoting neglected tropical disease. Alarmingly, climate change is accelerating the geographical spreading of CD causative parasite, Trypanosoma cruzi, which additionally increases infection rates. Still, CD treatment remains challenging due to a lack of safe and efficient drugs. In this work, we analyze the viability of T. cruzi Akt-like kinase (TcAkt) as drug target against CD including primary structural and functional information about a parasitic Akt protein. Nuclear Magnetic Resonance derived information in combination with Molecular Dynamics simulations offer detailed insights into structural properties of the pleckstrin homology (PH) domain of TcAkt and its binding to phosphatidylinositol phosphate ligands (PIP). Experimental data combined with Alpha Fold proposes a model for the mechanism of action of TcAkt involving a PIP-induced disruption of the intramolecular interface between the kinase and the PH domain resulting in an open conformation enabling TcAkt kinase activity. Further docking experiments reveal that TcAkt is recognized by human inhibitors PIT-1 and capivasertib, and TcAkt inhibition by UBMC-4 and UBMC-6 is achieved via binding to TcAkt kinase domain. Our in-depth structural analysis of TcAkt reveals potential sites for drug development against CD, located at activity essential regions.

Elucidating the structural basis of vitamin B12 derivatives as novel potent inhibitors of PTP1B: Insights from inhibitory mechanisms using Gaussian accelerated molecular dynamics (GaMD) and in vitro study

Vitamin B12 is a group of biologically active cobalamin compounds. In this study, we investigated the inhibitory effects of methylcobalamin (MeCbl) and hydroxocobalamin acetate (OHCbl Acetate) on protein tyrosine phosphatase 1B (PTP1B). MeCbl and OHCbl Acetate exhibited an IC50 of approximately 58.390 ± 2.811 μM and 8.998 ± 0.587 μM, respectively. The Ki values of MeCbl and OHCbl Acetate were 25.01 μM and 4.04 μM respectively. To elucidate the inhibition mechanism, we conducted a 500 ns Gaussian accelerated molecular dynamics (GaMD) simulation. Utilizing PCA and tICA, we constructed Markov state models (MSM) to examine secondary structure changes during motion. Our findings revealed that the α-helix at residues 37-42 remained the most stable in the PTP1B-OHCbl Acetate system. Furthermore, upon binding of OHCbl Acetate or MeCbl, the WPD loop of PTP1B moved inward to the active pocket, forming a closed conformation and potentially obstructs substrate entry. Protein-ligand interaction analysis and MM-PBSA showed that OHCbl Acetate exhibited lower binding free energy and engaged in more residue interactions with PTP1B. In summary, our study confirmed the substantial inhibitory activity of OHCbl Acetate against PTP1B, with its inhibitory potency notably surpassing that of MeCbl. We demonstrated potential molecular mechanisms of OHCbl Acetate inhibiting PTP1B.

Binding Mechanism of Inhibitors to BRD4 and BRD9 Decoded by Multiple Independent Molecular Dynamics Simulations and Deep Learning

Bromodomain 4 and 9 (BRD4 and BRD9) have been regarded as important targets of drug designs in regard to the treatment of multiple diseases. In our current study, molecular dynamics (MD) simulations, deep learning (DL) and binding free energy calculations are integrated to probe the binding modes of three inhibitors (H1B, JQ1 and TVU) to BRD4 and BRD9. The MD trajectory-based DL successfully identify significant functional function domains, such as BC-loop and ZA-loop. The information from the post-processing analysis of MD simulations indicates that inhibitor binding highly influences the structural flexibility and dynamic behavior of BRD4 and BRD9. The results of the MM-GBSA calculations not only suggest that the binding ability of H1B, JQ1 and TVU to BRD9 are stronger than to BRD4, but they also verify that van der Walls interactions are the primary forces responsible for inhibitor binding. The hot spots of BRD4 and BRD9 revealed by residue-based free energy estimation provide target sites of drug design in regard to BRD4 and BRD9. This work is anticipated to provide useful theoretical aids for the development of selective inhibitors over BRD family members.

PaCS-Toolkit: Optimized Software Utilities for Parallel Cascade Selection Molecular Dynamics (PaCS-MD) Simulations and Subsequent Analyses

Parallel cascade selection molecular dynamics (PaCS-MD) is an enhanced conformational sampling method conducted as a "repetition of time leaps in parallel worlds", comprising cycles of multiple molecular dynamics (MD) simulations performed in parallel and selection of the initial structures of MDs for the next cycle. We developed PaCS-Toolkit, an optimized software utility enabling the use of different MD software and trajectory analysis tools to facilitate the execution of the PaCS-MD simulation and analyze the obtained trajectories, including the preparation for the subsequent construction of the Markov state model. PaCS-Toolkit is coded with Python, is compatible with various computing environments, and allows for easy customization by editing the configuration file and specifying the MD software and analysis tools to be used. We present the software design of PaCS-Toolkit and demonstrate applications of PaCS-MD variations: original targeted PaCS-MD to peptide folding; rmsdPaCS-MD to protein domain motion; and dissociation PaCS-MD to ligand dissociation from adenosine A2A receptor.

MPI-parallelization of the grid inhomogeneous solvation theory calculation

The grid inhomogeneous solvation theory (GIST) method requires the often time-consuming calculation of water-water and water-solute energy on a grid. Previous efforts to speed up this calculation include using OpenMP, GPUs, and particle mesh Ewald. This article details how the speed of this calculation can be increased by parallelizing it with MPI, where trajectory frames are divided among multiple processors. This requires very little communication between individual processes during trajectory processing, meaning the calculation scales well to large processor counts. This article also details how the entropy calculation, which must happen after trajectory processing since it requires information from all trajectory frames, is parallelized via MPI. This parallelized GIST method has been implemented in the freely-available CPPTRAJ analysis software.

Calculations of Absolute Free Energies, Enthalpies, and Entropies for Drug Binding

Computer simulation methods can aid in the rational design of drugs aimed at a specific target, typically a protein. The affinity of a drug for its target is given by the free energy of binding. Binding can be further characterized by the enthalpy and entropy changes in the process. Methods exist to determine exact free energies, enthalpies, and entropies that are dependent only on the quality of the potential model and adequate sampling of conformational degrees of freedom. Entropy and enthalpy are roughly an order of magnitude more difficult to calculate than the free energy. This project combines a replica exchange method for enhanced sampling, designed to be efficient for protein-sized systems, with free energy calculations. This approach, replica exchange with dynamical scaling (REDS), uses two conventional simulations at different temperatures so that the entropy can be found from the temperature dependence of the free energy. A third replica is placed between them, with a modified Hamiltonian that allows it to span the temperature range of the conventional replicas. REDS provides temperature-dependent data and aids in sampling. It is applied to the bromodomain-containing protein 4 (BRD4) system. We find that for the force fields used, the free energies are accurate but the entropies and enthalpies are not, with the entropic contribution being too positive. Reproducing the entropy and enthalpy of binding appears to be a more stringent test of the force fields than reproducing the free energy.

De novo design of drug-binding proteins with predictable binding energy and specificity

The de novo design of small molecule-binding proteins has seen exciting recent progress; however, high-affinity binding and tunable specificity typically require laborious screening and optimization after computational design. We developed a computational procedure to design a protein that recognizes a common pharmacophore in a series of poly(ADP-ribose) polymerase-1 inhibitors. One of three designed proteins bound different inhibitors with affinities ranging from <5 nM to low micromolar. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free energy calculations performed directly on the designed models were in excellent agreement with the experimentally measured affinities. We conclude that de novo design of high-affinity small molecule-binding proteins with tuned interaction energies is feasible entirely from computation.

Prognostic Model Construction of Disulfidptosis-Related Genes and Targeted Anticancer Drug Research in Pancreatic Cancer

Pancreatic cancer stands as one of the most lethal malignancies, characterized by delayed diagnosis, high mortality rates, limited treatment efficacy, and poor prognosis. Disulfidptosis, a recently unveiled modality of cell demise induced by disulfide stress, has emerged as a critical player intricately associated with the onset and progression of various cancer types. It has emerged as a promising candidate biomarker for cancer diagnosis, prognosis assessment, and treatment strategies. In this study, we have effectively established a prognostic risk model for pancreatic cancer by incorporating multiple differentially expressed long non-coding RNAs (DElncRNAs) closely linked to disulfide-driven cell death. Our investigation delved into the nuanced relationship between the DElncRNA-based predictive model for disulfide-driven cell death and the therapeutic responses to anticancer agents. Our findings illuminate that the high-risk subgroup exhibits heightened susceptibility to the small molecule compound AZD1208, positioning it as a prospective therapeutic agent for pancreatic cancer. Finally, we have elucidated the underlying mechanistic potential of AZD1208 in ameliorating pancreatic cancer through its targeted inhibition of the peroxisome proliferator-activated receptor-γ (PPARG) protein, employing an array of comprehensive analytical methods, including molecular docking and molecular dynamics (MD) simulations. This study explores disulfidptosis-related genes, paving the way for the development of targeted therapies for pancreatic cancer and emphasizing their significance in the field of oncology. Furthermore, through computational biology approaches, the drug AZD1208 was identified as a potential treatment targeting the PPARG protein for pancreatic cancer. This discovery opens new avenues for exploring targets and screening drugs for pancreatic cancer.

Reengineering of a flavin-binding fluorescent protein using ProteinMPNN

Recent advances in machine learning techniques have led to development of a number of protein design and engineering approaches. One of them, ProteinMPNN, predicts an amino acid sequence that would fold and match user-defined backbone structure. Its performance was previously tested for proteins composed of standard amino acids, as well as for peptide- and protein-binding proteins. In this short report, we test whether ProteinMPNN can be used to reengineer a non-proteinaceous ligand-binding protein, flavin-based fluorescent protein CagFbFP. We fixed the native backbone conformation and the identity of 20 amino acids interacting with the chromophore (flavin mononucleotide, FMN) while letting ProteinMPNN predict the rest of the sequence. The software package suggested replacing 36-48 out of the remaining 86 amino acids so that the resulting sequences are 55%-66% identical to the original one. The three designs that we tested experimentally displayed different expression levels, yet all were able to bind FMN and displayed fluorescence, thermal stability, and other properties similar to those of CagFbFP. Our results demonstrate that ProteinMPNN can be used to generate diverging unnatural variants of fluorescent proteins, and, more generally, to reengineer proteins without losing their ligand-binding capabilities.

Identifying novel aryl hydrocarbon receptor (AhR) modulators from clinically approved drugs: In silico screening and In vitro validation

The aryl hydrocarbon receptor (AhR) functions as a vital ligand-activated transcription factor, governing both physiological and pathophysiological processes. Notably, it responds to xenobiotics, leading to a diverse array of outcomes. In the context of drug repurposing, we present here a combined approach of utilizing structure-based virtual screening and molecular dynamics simulations. This approach aims to identify potential AhR modulators from Drugbank repository of clinically approved drugs. By focusing on the AhR PAS-B binding pocket, our screening protocol included binding affinities calculations, complex stability, and interactions within the binding site as a filtering method. Comprehensive evaluations of all DrugBank small molecule database revealed ten promising hits. This included flibanserin, butoconazole, luliconazole, naftifine, triclabendazole, rosiglitazone, empagliflozin, benperidol, nebivolol, and zucapsaicin. Each exhibiting diverse binding behaviors and remarkably very low binding free energy. Experimental studies further illuminated their modulation of AhR signaling, and showing that they are consistently reducing AhR activity, except for luliconazole, which intriguingly enhances the AhR activity. This work demonstrates the possibility of using computational modelling as a quick screening tool to predict new AhR modulators from extensive drug libraries. Importantly, these findings hold immense therapeutic potential for addressing AhR-associated disorders. Consequently, it offers compelling prospects for innovative interventions through drug repurposing.

In silico investigation of falcipain-2 inhibition by hybrid benzimidazole-thiosemicarbazone antiplasmodial agents: A molecular docking, molecular dynamics simulation, and kinetics study

The emergence of artemisinin-resistant variants of Plasmodium falciparum necessitates the urgent search for novel antimalarial drugs. In this regard, an in silico study to screen antimalarial drug candidates from a series of benzimidazole-thiosemicarbazone hybrid molecules with interesting antiplasmodial properties and explore their falcipain-2 (FP2) inhibitory potentials has been undertaken herein. FP2 is a key cysteine protease that degrades hemoglobin in Plasmodium falciparum and is an important biomolecular target in the development of antimalarial drugs. Pharmacokinetic properties, ADMET profiles, MM/GBSA-based binding free energies, reaction mechanisms, and associated barrier heights have been investigated. DFT, molecular dynamics simulation, molecular docking, and ONIOM methods were used. From the results obtained, four 4N-substituted derivatives of the hybrid molecule (E)-2-(1-(5-chloro-1H-benzo[d]imidazol-2-yl)ethylidene)hydrazine-1-carbothioamide (1A) denoted 1B, 1C, 1D, and 1E are drug-like and promising inhibitors of FP2, exhibiting remarkably small inhibitory constants (5.94 × 10-14 - 2.59 × 10-04 n M) and favorable binding free energies (-30.32 to -17.17 kcal/mol). Moreover, the ONIOM results have revealed that 1B and possibly 1C and 1D may act as covalent inhibitors of FP2. The rate-determining step of the thermodynamically favorable covalent binding mechanism occurs across a surmountable barrier height of 24.18 kcal/mol in water and 28.42 kcal/mol in diethyl ether. Our findings are useful for further experimental investigations on the antimalarial activities of the hybrid molecules studied.

Evaluation of Adenanthera pavonina-derived compounds against diabetes mellitus: insight into the phytochemical analysis and in silico assays

Adenanthera pavonina is a medicinal plant with numerous potential secondary metabolites showing a significant level of antidiabetic activity. The objective of the current study was to identify potential phytochemicals from the methanolic leaf extract of Adenanthera pavonina as therapeutic agents against diabetes mellitus using GC-MS and in silico methods. The GC-MS analysis of the leaf extract revealed a total of 17 phytochemicals. Molecular docking was performed using these phytochemicals, targeting the mutated insulin receptor tyrosine kinase (5hhw), which inhibits glucose uptake by cells. Diazoprogesterone (-9.2 kcal/mol), 2,4,4,7a-Tetramethyl-1-(3-oxobutyl)octahydro-1H-indene-2-carboxylic acid (-6.9 kcal/mol), and 2-Naphthalenemethanol, decahydro-.alpha.,.alpha.,4a-trimethyl-8-methylene-, [2R-(2.alpha.,4a.alpha.,8a.beta.)] (-6.6 kcal/mol) exhibited better binding with the target protein. The ADMET analysis was performed for the top three compounds with the best docking scores, which showed positive results with no observed toxicity in the AMES test. Furthermore, the molecular dynamics study confirmed the favorable binding of Diazoprogesterone, 2,4,4,7a-Tetramethyl-1-(3-oxobutyl)octahydro-1H-indene-2-carboxylic acid and 2-Naphthalenemethanol, decahydro-.alpha.,.alpha.,4a-trimethyl-8-methylene-, [2R-(2.alpha.,4a.alpha.,8a.beta.)] with the receptor throughout the 100 ns simulation period.

Molecular and Structural Basis for Cγ-C Bond Formation by PLP-Dependent Enzyme Fub7

Pyridoxal 5'-phosphate (PLP)-dependent enzymes that catalyze γ-replacement reactions are prevalent, yet their utilization of carbon nucleophile substrates is rare. The recent discovery of two PLP-dependent enzymes, CndF and Fub7, has unveiled unique C-C bond forming capabilities, enabling the biocatalytic synthesis of alkyl- substituted pipecolic acids from O-acetyl-L-homoserine and β-keto acid or aldehyde derived enolates. This breakthrough presents fresh avenues for the biosynthesis of pipecolic acid derivatives. However, the catalytic mechanisms of these enzymes remain elusive, and a dearth of structural information hampers their extensive application. Here, we have broadened the catalytic scope of Fub7 by employing ketone-derived enolates as carbon nucleophiles, revealing Fub7's capacity for substrate-dependent regioselective α-alkylation of unsymmetrical ketones. Through an integrated approach combining X-ray crystallography, spectroscopy, mutagenesis, and computational docking studies, we offer a detailed mechanistic insight into Fub7 catalysis. Our findings elucidate the structural basis for its substrate specificity, stereoselectivity, and regioselectivity. Our work sets the stage ready for subsequent protein engineering effort aimed at expanding the synthetic utility of Fub7, potentially unlocking novel methods to access a broader array of noncanonical amino acids.

Membrane-assisted tariquidar access and binding mechanisms of human ATP-binding cassette transporter P-glycoprotein

The human multidrug transporter P-glycoprotein (P-gp) is physiologically essential and of key relevance to biomedicine. Recent structural studies have shed light on the mode of inhibition of the third-generation inhibitors for human P-gp, but the molecular mechanism by which these inhibitors enter the transmembrane sites remains poorly understood. In this study, we utilized all-atom molecular dynamics (MD) simulations to characterize human P-gp dynamics under a potent inhibitor, tariquidar, bound condition, as well as the atomic-level binding pathways in an explicit membrane/water environment. Extensive unbiased simulations show that human P-gp remains relatively stable in tariquidar-free and bound states, while exhibiting a high dynamic binding mode at either the drug-binding pocket or the regulatory site. Free energy estimations by partial nudged elastic band (PNEB) simulations and Molecular Mechanics Generalized Born Surface Area (MM/GBSA) method identify two energetically favorable binding pathways originating from the cytoplasmic gate with an extended tariquidar conformation. Interestingly, free tariquidar in the lipid membrane predominantly adopts extended conformations similar to those observed at the regulatory site. These results suggest that membrane lipids may preconfigure tariquidar into an active ligand conformation for efficient binding to the regulatory site. However, due to its conformational plasticity, tariquidar ultimately moves toward the drug-binding pocket in both pathways, explaining how it acts as a substrate at low concentrations. Our molecular findings propose a membrane-assisted mechanism for the access and binding of the third-generation inhibitors to the binding sites of human P-gp, and offer deeper insights into the molecule design of more potent inhibitors against P-gp-mediated drug resistance.

Oligo-PROTAC strategy for cell-selective and targeted degradation of activated STAT3

Decoy oligodeoxynucleotides (ODNs) allow targeting undruggable transcription factors, such as STAT3, but their limited potency and lack of delivery methods hampered translation. To overcome these challenges, we conjugated a STAT3-specific decoy to thalidomide, a ligand to cereblon in E3 ubiquitin ligase complex, to generate a proteolysis-targeting chimera (STAT3DPROTAC). STAT3DPROTAC downregulated STAT3 in target cells, but not STAT1 or STAT5. Computational modeling of the STAT3DPROTAC ternary complex predicted two surface lysines, K601 and K626, in STAT3 as potential ubiquitination sites. Accordingly, K601/K626 point mutations in STAT3, as well as proteasome inhibition or cereblon deletion, alleviated STAT3DPROTAC effect. Next, we conjugated STAT3DPROTAC to a CpG oligonucleotide targeting Toll-like receptor 9 (TLR9) to generate myeloid/B cell-selective C-STAT3DPROTAC. Naked C-STAT3DPROTAC was spontaneously internalized by TLR9+ myeloid cells, B cells, and human and mouse lymphoma cells but not by T cells. C-STAT3DPROTAC effectively decreased STAT3 protein levels and also STAT3-regulated target genes critical for lymphoma cell proliferation and/or survival (BCL2L1, CCND2, and MYC). Finally, local C-STAT3DPROTAC administration to human Ly3 lymphoma-bearing mice triggered tumor regression, while control C-STAT3D and C-SCR treatments had limited effects. Our results underscore the feasibility of using a PROTAC strategy for cell-selective, decoy oligonucleotide-based STAT3 targeting of and potentially other tumorigenic transcription factors for cancer therapy.

First report on chemometrics-driven multilayered lead prioritization in addressing oxysterol-mediated overexpression of G protein-coupled receptor 183

Contemporary research has convincingly demonstrated that upregulation of G protein-coupled receptor 183 (GPR183), orchestrated by its endogenous agonist, 7α,25-dihydroxyxcholesterol (7α,25-OHC), leads to the development of cancer, diabetes, multiple sclerosis, infectious, and inflammatory diseases. A recent study unveiled the cryo-EM structure of 7α,25-OHC bound GPR183 complex, presenting an untapped opportunity for computational exploration of potential GPR183 inhibitors, which served as our inspiration for the current work. A predictive and validated two-dimensional QSAR model using genetic algorithm (GA) and multiple linear regression (MLR) on experimental GPR183 inhibition data was developed. QSAR study highlighted that structural features like dissimilar electronegative atoms, quaternary carbon atoms, and CH2RX fragment (X: heteroatoms) influence positively, while the existence of oxygen atoms with a topological separation of 3, negatively affects GPR183 inhibitory activity. Post assessment of true external set prediction capability, the MLR model was deployed to screen 12,449 DrugBank compounds, followed by a screening pipeline involving molecular docking, druglikeness, ADMET, protein-ligand stability assessment using deep learning algorithm, molecular dynamics, and molecular mechanics. The current findings strongly evidenced DB05790 as a potential lead for prospective interference of oxysterol-mediated GPR183 overexpression, warranting further in vitro and in vivo validation.

The novel non-hallucinogenic compound DM506 (3-methyl-1,2,3,4,5,6-hexahydroazepino[4,5-b]indole) induces sedative- and anxiolytic-like activity in mice by a mechanism involving 5-HT2A receptor activation

The anxiolytic and sedative-like effects of 3-methyl-1,2,3,4,5,6-hexahydroazepino[4,5-b]indole (DM506), a non-hallucinogenic compound derived from ibogamine, were studied in mice. The behavioral effects were examined using Elevated O-maze and novelty suppressed feeding (NSFT) tests, open field test, and loss of righting reflex (LORR) test. The results showed that 15 mg/kg DM506 induced acute and long-lasting anxiolytic-like activity in naive and stressed/anxious mice, respectively. Repeated administration of 5 mg/kg DM506 did not cause cumulative anxiolytic activity or any side effects. Higher doses of DM506 (40 mg/kg) induced sedative-like activity, which was inhibited by a selective 5-HT2A receptor antagonist, volinanserin. Electroencephalography results showed that 15 mg/kg DM506 fumarate increased the transition from a highly alert state (fast γ wavelength) to a more synchronized deep-sleeping activity (δ wavelength), which is reflected in the sedative/anxiolytic activity in mice but without the head-twitch response observed in hallucinogens. The functional, radioligand binding, and molecular docking results showed that DM506 binds to the agonist sites of human 5-HT2A (Ki = 24 nM) and 5-HT2B (Ki = 16 nM) receptors and activates them with a potency (EC50) of 9 nM and 3 nM, respectively. DM506 was relatively less potent and behaved as a partial agonist (efficacy <80%) for both receptor subtypes compared to the full agonist DOI (2,5-dimethoxy-4-iodoamphetamine). Our study showed for the first time that the non-hallucinogenic compound DM506 induces anxiolytic- and sedative-like activities in naïve and stressed/anxious mice in a dose-, time-, and volinanserin-sensitive manner, likely through mechanisms involving 5-HT2A receptor activation.

Mechanistic insights into ligand dissociation from the SARS-CoV-2 spike glycoprotein

The COVID-19 pandemic, driven by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spurred an urgent need for effective therapeutic interventions. The spike glycoprotein of the SARS-CoV-2 is crucial for infiltrating host cells, rendering it a key candidate for drug development. By interacting with the human angiotensin-converting enzyme 2 (ACE2) receptor, the spike initiates the infection of SARS-CoV-2. Linoleate is known to bind the spike glycoprotein, subsequently reducing its interaction with ACE2. However, the detailed mechanisms underlying the protein-ligand interaction remain unclear. In this study, we characterized the pathways of ligand dissociation and the conformational changes associated with the spike glycoprotein by using ligand Gaussian accelerated molecular dynamics (LiGaMD). Our simulations resulted in eight complete ligand dissociation trajectories, unveiling two distinct ligand unbinding pathways. The preference between these two pathways depends on the gate distance between two α-helices in the receptor binding domain (RBD) and the position of the N-linked glycan at N343. Our study also highlights the essential contributions of K417, N121 glycan, and N165 glycan in ligand unbinding, which are equally crucial in enhancing spike-ACE2 binding. We suggest that the presence of the ligand influences the motions of these residues and glycans, consequently reducing accessibility for spike-ACE2 binding. These findings enhance our understanding of ligand dissociation from the spike glycoprotein and offer significant implications for drug design strategies in the battle against COVID-19.